Molecular Docking (Classic & AI-Based): Theory and Practice

In this course you will learn the Molecular Docking subject in drug design. This course covers the theoretical aspects and practical aspects of molecular docking, including both classical and AI-based approaches.In the theoretical part, you will learn via lectures the main concepts of molecular docking, the applications in drug design, and the docking algorithms including scoring functions and search algorithms. The lectures focus on important aspects of molecular docking and also build intuition about the subject in addition to improving general drug design understanding. Both classical docking and AI-based docking methods are covered in this course.In the practical part, you will perform practical experiments (using freely available software and web servers) that cover what you learn in the theoretical part. You will be provided all the input datasets and files required for each experiment, in addition to step-by-step demonstrations.Theoretical PartIn section 1, you will learn the main concepts of molecular docking in drug design, docking algorithms including scoring functions and search algorithms, and the accuracy validation process through re-docking.In section 2, you will learn about AI-based docking methods, including an overview of generative AI approaches and the underlying algorithms of state-of-the-art methods such as DiffDock.Practical PartIn the first experiment, you will perform a step-by-step re-docking process to validate docking accuracy using the AutoDock Vina algorithm. This experiment covers the whole process in detail including target protein retrieval, structural preparation, performing docking, and docking result analysis.In the second experiment, you will perform a docking process for a new ligand. This experiment covers the whole process including preparing the ligand, performing the docking, and analyzing the binding poses and inte